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Inference of biophysical diffusion w...

Bernstein, Jason.

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Inference of biophysical diffusion with transient binding using particle filters and stochastic EM.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Inference of biophysical diffusion with transient binding using particle filters and stochastic EM./
Author:	Bernstein, Jason.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
Description:	141 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-04(E), Section: B.
Contained By:	Dissertation Abstracts International78-04B(E).
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10296935
ISBN:	9781369402902

Inference of biophysical diffusion with transient binding using particle filters and stochastic EM.
Bernstein, Jason.

Inference of biophysical diffusion with transient binding using particle filters and stochastic EM. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 141 p.

Source: Dissertation Abstracts International, Volume: 78-04(E), Section: B.

Thesis (Ph.D.)--The Pennsylvania State University, 2016.

Many biophysical processes exhibit switching between free and bound diffusive regimes. For example, motor proteins diffusing along a microtubule can, under certain experimental conditions, become transiently bound to the microtubule, and other types of switching behavior have been observed on larger multimotor scales in vivo. This thesis proposes a general modeling framework for biophysical diffusion with transient binding and describes an inferential approach for parameter estimation. In particular, the model specifies Markovian switching between an overdamped Langevin equation in the bound regime and free Brownian diffusion in the unbound regime. Initially assuming a quadratic potential in the bound regime, the unobserved regime of the particle and binding site locations are predicted with a particle filter and model parameters are estimated with a stochastic EM algorithm. The inferential approach is then modified to estimate additive potential functions and the utility of this approach is demonstrated on the worm-like chain model. Last, we consider estimation of a non-standard regime switching model specifying a Kinesin and Dynein motor protein attached to a cargo by linear springs.

ISBN: 9781369402902Subjects--Topical Terms:

517247
Statistics.

Inference of biophysical diffusion with transient binding using particle filters and stochastic EM.
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245 1 0 $a Inference of biophysical diffusion with transient binding using particle filters and stochastic EM.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2016
300 $a 141 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-04(E), Section: B.
500 $a Adviser: John Fricks.
502 $a Thesis (Ph.D.)--The Pennsylvania State University, 2016.
520 $a Many biophysical processes exhibit switching between free and bound diffusive regimes. For example, motor proteins diffusing along a microtubule can, under certain experimental conditions, become transiently bound to the microtubule, and other types of switching behavior have been observed on larger multimotor scales in vivo. This thesis proposes a general modeling framework for biophysical diffusion with transient binding and describes an inferential approach for parameter estimation. In particular, the model specifies Markovian switching between an overdamped Langevin equation in the bound regime and free Brownian diffusion in the unbound regime. Initially assuming a quadratic potential in the bound regime, the unobserved regime of the particle and binding site locations are predicted with a particle filter and model parameters are estimated with a stochastic EM algorithm. The inferential approach is then modified to estimate additive potential functions and the utility of this approach is demonstrated on the worm-like chain model. Last, we consider estimation of a non-standard regime switching model specifying a Kinesin and Dynein motor protein attached to a cargo by linear springs.
590 $a School code: 0176.
650 4 $a Statistics. $3 517247
650 4 $a Biophysics. $3 518360
690 $a 0463
690 $a 0786
710 2 $a The Pennsylvania State University. $3 699896
773 0 $t Dissertation Abstracts International $g 78-04B(E).
790 $a 0176
791 $a Ph.D.
792 $a 2016
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10296935